Articles liés à Data Science with Python and Dask
Synopsis
Summary
Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book.
About the Technology
An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.
About the Book
Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.
What's inside
- Working with large, structured and unstructured datasets
- Visualization with Seaborn and Datashader
- Implementing your own algorithms
- Building distributed apps with Dask Distributed
- Packaging and deploying Dask apps
About the Reader
For data scientists and developers with experience using Python and the PyData stack.
About the Author
Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.
Table of Contents
- Why scalable computing matters
- Introducing Dask
- Introducing Dask DataFrames
- Loading data into DataFrames
- Cleaning and transforming DataFrames
- Summarizing and analyzing DataFrames
- Visualizing DataFrames with Seaborn
- Visualizing location data with Datashader
- Working with Bags and Arrays
- Machine learning with Dask-ML
- Scaling and deploying Dask
PART 1 - The Building Blocks of scalable computing
PART 2 - Working with Structured Data using Dask DataFrames
PART 3 - Extending and deploying Dask
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Data Science with Python and Dask
Edité par
Manning Publications, 2019
ISBN 10 : 1617295604
ISBN 13 : 9781617295607
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Data Science with Python and Dask
Edité par
Manning Publications, 2019
ISBN 10 : 1617295604
ISBN 13 : 9781617295607
Ancien ou d'occasion
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Data Science with Python and Dask
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ISBN 13 : 9781617295607
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Data Science with Python and Dask
Edité par
Manning (edition First Edition), 2019
ISBN 10 : 1617295604
ISBN 13 : 9781617295607
Ancien ou d'occasion
Paperback
Edition originale
Vendeur : BooksRun, Philadelphia, PA, Etats-Unis
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Paperback. Etat : Very Good. First Edition. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported. N° de réf. du vendeur 1617295604-8-1
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ISBN 13 : 9781617295607
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DATA SCIENCE AT SCALE WITH PYTHON & DASK
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ISBN 10 : 1617295604
ISBN 13 : 9781617295607
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Paperback. Etat : New. Large datasets tend to be distributed, non-uniform, and prone to change. Dask simplifies the process of ingesting, filtering, and transforming data, reducing or eliminating the need for a heavyweight framework like Spark. Data Science at Scale with Python and Dask teaches readers how to build distributed data projects that can handle huge amounts of data. The book introduces Dask Data Frames and teaches helpful code patterns to streamline the reader's analysis. Key Features Working with large structured datasets Writing DataFrames Cleaningand visualizing DataFrames Machine learning with Dask-ML Working with Bags and Arrays Written for data engineers and scientists with experience using Python. Knowledge of the PyData stack (Pandas, NumPy, and Scikit-learn) will be helpful. No experience with low-level parallelism is required. About the technology Dask is a self-contained, easily extendible library designed to query, stream, filter, and consolidate huge datasets. Jesse Daniel has five years of experience writing applications in Python, including three years working with in the PyData stack (Pandas, NumPy, SciPy, Scikit-Learn). Jesse joined the faculty of the University of Denver in 2016 as an adjunct professor of business information and analytics, where he currently teaches a Python for Data Science course. N° de réf. du vendeur LU-9781617295607
Quantité disponible : 10 disponible(s)
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Data Science at Scale with Python and Dask
Edité par
Manning Publications, US, 2019
ISBN 10 : 1617295604
ISBN 13 : 9781617295607
Neuf
Paperback
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Paperback. Etat : New. Large datasets tend to be distributed, non-uniform, and prone to change. Dask simplifies the process of ingesting, filtering, and transforming data, reducing or eliminating the need for a heavyweight framework like Spark. Data Science at Scale with Python and Dask teaches readers how to build distributed data projects that can handle huge amounts of data. The book introduces Dask Data Frames and teaches helpful code patterns to streamline the reader's analysis. Key Features Working with large structured datasets Writing DataFrames Cleaningand visualizing DataFrames Machine learning with Dask-ML Working with Bags and Arrays Written for data engineers and scientists with experience using Python. Knowledge of the PyData stack (Pandas, NumPy, and Scikit-learn) will be helpful. No experience with low-level parallelism is required. About the technology Dask is a self-contained, easily extendible library designed to query, stream, filter, and consolidate huge datasets. Jesse Daniel has five years of experience writing applications in Python, including three years working with in the PyData stack (Pandas, NumPy, SciPy, Scikit-Learn). Jesse joined the faculty of the University of Denver in 2016 as an adjunct professor of business information and analytics, where he currently teaches a Python for Data Science course. N° de réf. du vendeur LU-9781617295607
Quantité disponible : 10 disponible(s)